Prof. László Horváth
Óbuda University, Hungary

László Horváth is a university professor of virtual engineering systems at the John von Neumann Faculty of Informatics, Óbuda University. He received the Ph.D. degree from the Hungarian Academy of Sciences in 1993 and from the Budapest University of Technology and Economics in 2004 in engineering modeling and simulation. He filled several research and higher education leader positions during the past three decades. He presently serves as managing director and one of the core members of the Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University. His recent internationally published research interests include integrated theory and experience representation based system level object models, two-way driving connection between model and cyber units of cyber-physical system, modeling complex engineering structure as multidisciplinary system, and organized active intellectual property based driving structures for objects in engineering model system. Recently, he founded the Virtual Research Laboratory for experimental model based PhD and other university fundamental and application research in systems-organized and situation-controlled complex engineering structures. He authored and coauthored more than three hundred international scientific publications. He received near four hundred independent citations for the above publications from international authors. His Hirsch index is 9.

Prof. Rung-Ching Chen
Chaoyang University of Technology, Taiwan

Rung-Ching Chen received the B.S. degree from the department of electrical engineering and the M. S. degree from the institute of computer engineering both are from National Taiwan University of Science and Technology, Taipei, Taiwan. In 1998, he received the Ph.D. degree from the department of applied mathematics in computer science sessions, National Chung Tsing University. He is now a distinguished professor at the Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan. He was a Department Chair of Information Management in CYUT, Chaoyang University of Technology, between 2005 and 2007. He has been a Dean of Informatics College of CYUT since 2007 to 2015 and the Execution Director of Institution Research Office during Dec. 2015 and Jan. 2017 in CYUT. He also the President of Taiwanese Association for Consumer Electronics during Dec. 2012 and Dec. 2014. He has been a Fellow of IET since July 2011. He was a visiting Professor at University of Central Florida, U.S.A. in 2012 and a visiting researcher at the University of Aizu, Japan in 2017. He has executed many enterprises and national projects and has published many SCI journal papers. He also served many journal papers as editors or associated editors and the chief of many conferences. His research interests include networks technology, domain ontology, machine learning, IoT and data analysis, and the applications of Artificial Intelligent.

Speech Title: From Objects Detection to Improving Road Information Recognition Based on Deep Learning Models
Abstract: Traffic sign identification is necessary for a real-world application, including traffic control, autonomous driving, driver protection, traffic accident investigations, and road service. In traffic sign recognition systems (TSRS), two related items are detection and recognition. Further, traffic sign recognition (TSR) offers valuable knowledge for smart cars, including advanced driving assistance (ADAS) and cooperative intelligent transport systems (CITS) guidance and warnings. However, traffic signs are challenging to detect using an exact and real-time approach in practical autonomous driving scenes. Identifying small traffic signs is a challenging issue in object recognition, and its precision accuracy in the detection process is crucial to protecting an intelligent transportation system (ITS). This talk will propose an identification of traffic signs with adjustable anchors. Further, the SPP principle is utilized to upgrade the object detection method's backbone and extract features. Moreover, we adopt the SPP to learning object features more completely. First, at the detection stage, our work implements a color-based detection method to detect the sign in the image with various target sizes. Next, deep learning uses TSR employing spatial pyramid pooling (SPP) with scale and weight analysis. Finally, we evaluated our proposed methods on the Belgium traffic sign dataset (BTSD) and Taiwan traffic sign dataset (TWTSD). The experiment results show that our method upgrades the achievement of all experimental models.

Assoc. Prof. Yoritaka Iwata
Kansai University, Japan

Dr. Yoritaka Iwata is an associate professor at the Faculty of Chemistry, Materials and Bioengineering, Kansai University, Osaka, Japan. He received his B.Eng. from Osaka University, M.Eng. from Osaka University, Ph.D. in mathematics from Osaka University, Japan under the supervision of Professors K. Uosaki and A. Yagi, Ph.D. in physics from The University of Tokyo, Japan under the supervision of Professor T. Otsuka. Dr. Iwata has chaired or co-chaired several international workshops and is serving as a reviewer board member of mathemtics journal ”Axioms”, and a review editor of physics journal "Frontiers in Physics". His research interest is in functional analysis, operator theory, Numerical analysis, and mathematical physics including soliton theory, quantum physics/information. He mostly published in the areas of functional analysis, and nuclear soliton theory.

Speech Title: The Quantum Information Theory by High Precision Fourier-spectral Method
Abstract: Superposition of states leading to the entanglement of states is one of the important ingredients of quantum information theory. In this talk, starting with the basic concepts of quantum information theory, the nonlinear effects resulting from the interaction and the tunneling effects resulting from the quantum kinematics are evaluated focusing on the superposition of states being associated with the quantum coherence/decoherence. Based on the high precision Fourierspectral method, a class of nonlinear superposition principle is demonstrated by the Klein-Gordon type scalar field.